Technology Toolbox

Industrial performance and reliability

Smart process data analytics convert time-series data into strategic, sharable, actionable information. Seeq offers software specifically designed to analyze process manufacturing data. Seeq connects to data – including data from all leading process historians as well as contextual data from production and business systems – without moving or copying it. It can be installed on the premises or in the cloud or deployed as a service.

“Advanced analytics in manufacturing means putting innovations from machine learning and Big Data into the hands of front-line engineers,” says Michael Risse, VP at Seeq Corp. “The result is faster insights and time to impact ... by utilizing the expertise and experience of the employees who know the plant and its operations best.”

ContextHub from TrendMiner connects process and asset data from business-critical solutions. The software lets customers “bring together their time-series and context data from sources such as batch, quality, OEE, maintenance, ERP, shift log, and other systems in one self-service analytics platform to make data-driven decisions,” says Rob Azevedo, product owner at TrendMiner.

With ContextHub, chemical and process engineers can overlay related batch, quality, and maintenance information on their historical sensor data or use context on periods of downtime to exclude them during their analysis, Azevedo explains. They can see which assets in their plants experience the most downtime by gathering their availability data and analyzing worst performers based on data instead of a best guess.

FacilityConneX is developing and running smart analytics on chillers, boilers, HVAC equipment, fuel cells, and water-treatment plants to help customers optimize operational efficiencies and drive down the energy consumption of the equipment. Predictive models are formed from historical data; machine learning methods are developed and trained; and equipment domain knowledge completes the picture.

“We are using machine-learning techniques to continually optimize the analytics as conditions change at each customer,” says Tom Schiller, CEO and founder of FacilityConneX. “The analytics run in our multitenant AWS Cloud environment and are delivered to customers via our voice dashboards and our new mobile app.”

Scheduled for release in early 2019 is Uptake Asset Performance Management (APM), which integrates key features from Uptake’s own industrial predictive analytics and machine-learning software with a comprehensive industrial failure data library from Asset Performance Technologies (APT), a company acquired in 2018. Static and dynamic digital twins will be built to help optimize assets and their lifecycles.

About the Author: Sheila Kennedy

Sheila Kennedy, CMRP, is a professional freelance writer specializing in industrial and technical topics. She established Additive Communications in 2003 to serve software, technology, and service providers in industries such as manufacturing and utilities, and became a contributing editor and Technology Toolbox columnist for Plant Services in 2004. Prior to Additive Communications, she had 11 years of experience implementing industrial information systems. Kennedy earned her B.S. at Purdue University and her MBA at the University of Phoenix. She can be reached at sheila@addcomm.com.

Equipment reliability

Smart-motor drive analytics simplify and expedite motor troubleshooting. A traditional troubleshooting tool has been the portable oscilloscope. “These can often get to the root cause of the problem, but it takes a technician with specific experience on both the drives and oscilloscopes to be effective,” says Frank Healy, power quality applications specialist at Fluke Corp.

New MDA-500 Series motor drive analyzers from Fluke simplify the process by providing guiding measurements that automatically capture the information needed to get the motor drive running at peak performance, Healy says. The analyzers, which also function as wave-form analyzers and recording data loggers, reduce test times and improve test consistency, he says.

“A major value of analytics is knowing what you want to search for but doing it in a highly scalable and transposable manner,” says Robert Lee, CEO of Dragos. “So instead of just looking for anomalies in your environment and trying to chase them, it’s in defining scenarios that matter and addressing them when they occur.” He adds: “The same concepts apply to security, where we are seeing security analytics, in the form of threat analytics, overtake ‘anomaly detection’ due to the general noise and cost associated with running down every anomalous event.”